{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures.\n",
    "\n",
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive derivative of loss with respect to outputs and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_val:  (1000, 3, 32, 32)\n",
      "X_train:  (49000, 3, 32, 32)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "y_train:  (49000,)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.iteritems():\n",
    "    print '%s: ' % k, v.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: foward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.76984946819e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-9.\n",
    "print 'Testing affine_forward function:'\n",
    "print 'difference: ', rel_error(out, correct_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  3.37476127051e-09\n",
      "dw error:  3.69715901875e-11\n",
      "db error:  1.2749389372e-11\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-10\n",
    "print 'Testing affine_backward function:'\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dw error: ', rel_error(dw_num, dw)\n",
    "print 'db error: ', rel_error(db_num, db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU layer: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.99999979802e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-8\n",
    "print 'Testing relu_forward function:'\n",
    "print 'difference: ', rel_error(out, correct_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU layer: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.27562851365e-12\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-12\n",
    "print 'Testing relu_backward function:'\n",
    "print 'dx error: ', rel_error(dx_num, dx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward:\n",
      "dx error:  8.86850636856e-11\n",
      "dw error:  1.20673184651e-09\n",
      "db error:  1.51765768271e-11\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "print 'Testing affine_relu_forward:'\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dw error: ', rel_error(dw_num, dw)\n",
    "print 'db error: ', rel_error(db_num, db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.99946978663\n",
      "dx error:  1.40215660067e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.30253255714\n",
      "dx error:  7.97348030903e-09\n"
     ]
    }
   ],
   "source": [
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be 1e-9\n",
    "print 'Testing svm_loss:'\n",
    "print 'loss: ', loss\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be 2.3 and dx error should be 1e-8\n",
    "print '\\nTesting softmax_loss:'\n",
    "print 'loss: ', loss\n",
    "print 'dx error: ', rel_error(dx_num, dx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.83e-08\n",
      "W2 relative error: 3.20e-10\n",
      "b1 relative error: 9.83e-09\n",
      "b2 relative error: 4.33e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 2.53e-07\n",
      "W2 relative error: 7.98e-08\n",
      "b1 relative error: 1.56e-08\n",
      "b2 relative error: 9.09e-10\n"
     ]
    }
   ],
   "source": [
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-2\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print 'Testing initialization ... '\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print 'Testing test-time forward pass ... '\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print 'Testing training loss (no regularization)'\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "for reg in [0.0, 0.7]:\n",
    "    print 'Running numeric gradient check with reg = ', reg\n",
    "    model.reg = reg\n",
    "    loss, grads = model.loss(X, y)\n",
    "\n",
    "    for name in sorted(grads):\n",
    "        f = lambda _: model.loss(X, y)[0]\n",
    "        grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "        print '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 2070) loss: 2.302976\n",
      "(Epoch 0 / 10) train acc: 0.139000; val_acc: 0.140000\n",
      "(Iteration 101 / 2070) loss: 1.849689\n",
      "(Iteration 201 / 2070) loss: 1.682221\n",
      "(Epoch 1 / 10) train acc: 0.427000; val_acc: 0.413000\n",
      "(Iteration 301 / 2070) loss: 1.621092\n",
      "(Iteration 401 / 2070) loss: 1.624312\n",
      "(Epoch 2 / 10) train acc: 0.467000; val_acc: 0.452000\n",
      "(Iteration 501 / 2070) loss: 1.621148\n",
      "(Iteration 601 / 2070) loss: 1.377049\n",
      "(Epoch 3 / 10) train acc: 0.496000; val_acc: 0.465000\n",
      "(Iteration 701 / 2070) loss: 1.372313\n",
      "(Iteration 801 / 2070) loss: 1.450341\n",
      "(Epoch 4 / 10) train acc: 0.534000; val_acc: 0.485000\n",
      "(Iteration 901 / 2070) loss: 1.382154\n",
      "(Iteration 1001 / 2070) loss: 1.303802\n",
      "(Epoch 5 / 10) train acc: 0.532000; val_acc: 0.498000\n",
      "(Iteration 1101 / 2070) loss: 1.349909\n",
      "(Iteration 1201 / 2070) loss: 1.286409\n",
      "(Epoch 6 / 10) train acc: 0.530000; val_acc: 0.505000\n",
      "(Iteration 1301 / 2070) loss: 1.307726\n",
      "(Iteration 1401 / 2070) loss: 1.306668\n",
      "(Epoch 7 / 10) train acc: 0.579000; val_acc: 0.511000\n",
      "(Iteration 1501 / 2070) loss: 1.418747\n",
      "(Iteration 1601 / 2070) loss: 1.354241\n",
      "(Epoch 8 / 10) train acc: 0.574000; val_acc: 0.518000\n",
      "(Iteration 1701 / 2070) loss: 1.096708\n",
      "(Iteration 1801 / 2070) loss: 1.246297\n",
      "(Epoch 9 / 10) train acc: 0.597000; val_acc: 0.518000\n",
      "(Iteration 1901 / 2070) loss: 1.288102\n",
      "(Iteration 2001 / 2070) loss: 1.296761\n",
      "(Epoch 10 / 10) train acc: 0.579000; val_acc: 0.513000\n"
     ]
    }
   ],
   "source": [
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "\n",
    "model = TwoLayerNet()\n",
    "solver = Solver(model, data,\n",
    "                  update_rule='sgd',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  lr_decay=0.95,\n",
    "                  num_epochs=10, batch_size=236,\n",
    "                  print_every=100)\n",
    "solver.train()\n",
    "\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RFAZtCYzt2YSCl7feRkq0BWk67ChJREREREQ2LI9MQJUzTh49b5zX5rrmjR0liYiIiIjI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\n3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9qYpcICF9Y2VOKT1yzFhiYfNjRVobYiC603c11gHOg/Ed1aZgS1yeGp+3lKtUC25N3A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\nkiSlMEubJEmSJKUwS5skSZIkpbD/D9FZf97EDbsuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff5ccea790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial loss and gradient check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-6 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.30225591143\n",
      "W1 relative error: 7.97e-08\n",
      "W2 relative error: 7.43e-06\n",
      "W3 relative error: 7.87e-08\n",
      "b1 relative error: 4.24e-08\n",
      "b2 relative error: 4.02e-09\n",
      "b3 relative error: 1.33e-10\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.0790096677\n",
      "W1 relative error: 3.50e-08\n",
      "W2 relative error: 1.03e-07\n",
      "W3 relative error: 2.87e-07\n",
      "b1 relative error: 2.89e-08\n",
      "b2 relative error: 1.51e-08\n",
      "b3 relative error: 2.34e-10\n"
     ]
    }
   ],
   "source": [
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "    print 'Running check with reg = ', reg\n",
    "    model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "    loss, grads = model.loss(X, y)\n",
    "    print 'Initial loss: ', loss\n",
    "\n",
    "    for name in sorted(grads):\n",
    "        f = lambda _: model.loss(X, y)[0]\n",
    "        grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "        print '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. You will need to tweak the learning rate and initialization scale, but you should be able to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 20.970678\n",
      "(Epoch 0 / 20) train acc: 0.200000; val_acc: 0.087000\n",
      "(Epoch 1 / 20) train acc: 0.360000; val_acc: 0.110000\n",
      "(Epoch 2 / 20) train acc: 0.400000; val_acc: 0.126000\n",
      "(Epoch 3 / 20) train acc: 0.420000; val_acc: 0.157000\n",
      "(Epoch 4 / 20) train acc: 0.620000; val_acc: 0.137000\n",
      "(Epoch 5 / 20) train acc: 0.760000; val_acc: 0.126000\n",
      "(Iteration 11 / 40) loss: 3.038722\n",
      "(Epoch 6 / 20) train acc: 0.920000; val_acc: 0.132000\n",
      "(Epoch 7 / 20) train acc: 0.980000; val_acc: 0.142000\n",
      "(Epoch 8 / 20) train acc: 1.000000; val_acc: 0.141000\n",
      "(Epoch 9 / 20) train acc: 1.000000; val_acc: 0.143000\n",
      "(Epoch 10 / 20) train acc: 1.000000; val_acc: 0.144000\n",
      "(Iteration 21 / 40) loss: 0.003007\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.144000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.143000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.143000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.143000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.143000\n",
      "(Iteration 31 / 40) loss: 0.000764\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.144000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.144000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.144000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.144000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.144000\n"
     ]
    },
    {
     "data": {
      "image/png": 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3kzwtyZur6n75UjADAGAVNi9jnX+e5JuSfLy19oWqujTJ1aMti7Xu0NGZHDh8PCdOzmb7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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6873679e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 5e-2\n",
    "learning_rate = 1e-3\n",
    "model = FullyConnectedNet([100, 100],\n",
    "              weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again you will have to adjust the learning rate and weight initialization, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 3.879440\n",
      "(Epoch 0 / 20) train acc: 0.100000; val_acc: 0.089000\n",
      "(Epoch 1 / 20) train acc: 0.320000; val_acc: 0.125000\n",
      "(Epoch 2 / 20) train acc: 0.420000; val_acc: 0.126000\n",
      "(Epoch 3 / 20) train acc: 0.720000; val_acc: 0.134000\n",
      "(Epoch 4 / 20) train acc: 0.860000; val_acc: 0.138000\n",
      "(Epoch 5 / 20) train acc: 0.900000; val_acc: 0.128000\n",
      "(Iteration 11 / 40) loss: 0.884876\n",
      "(Epoch 6 / 20) train acc: 0.900000; val_acc: 0.129000\n",
      "(Epoch 7 / 20) train acc: 0.940000; val_acc: 0.134000\n",
      "(Epoch 8 / 20) train acc: 0.960000; val_acc: 0.140000\n",
      "(Epoch 9 / 20) train acc: 0.980000; val_acc: 0.135000\n",
      "(Epoch 10 / 20) train acc: 1.000000; val_acc: 0.145000\n",
      "(Iteration 21 / 40) loss: 0.114132\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.141000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.132000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.130000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.132000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.128000\n",
      "(Iteration 31 / 40) loss: 0.135759\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.137000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.135000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.135000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.136000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.133000\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAJbLRsLWnd39X4ZXAgCwhDYStq6tqpckuTrJP57YuHo5CAAATm8jYeuJp3xNkk7ypM0v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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f68757aad50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "learning_rate = 1e-2\n",
    "weight_scale = 5e-2\n",
    "model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inline question: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net?\n",
    "\n",
    "# Answer:\n",
    "Not really, they both seem to run fast.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochstic gradient descent.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than 1e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.88234703351e-09\n",
      "velocity error:  4.26928774328e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "print 'next_w error: ', rel_error(next_w, expected_next_w)\n",
    "print 'velocity error: ', rel_error(expected_velocity, config['velocity'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: 2.502618\n",
      "(Epoch 0 / 5) train acc: 0.109000; val_acc: 0.104000\n",
      "(Iteration 11 / 200) loss: 2.249609\n",
      "(Iteration 21 / 200) loss: 2.099600\n",
      "(Iteration 31 / 200) loss: 2.039301\n",
      "(Epoch 1 / 5) train acc: 0.278000; val_acc: 0.245000\n",
      "(Iteration 41 / 200) loss: 2.030475\n",
      "(Iteration 51 / 200) loss: 1.845101\n",
      "(Iteration 61 / 200) loss: 2.039069\n",
      "(Iteration 71 / 200) loss: 1.829492\n",
      "(Epoch 2 / 5) train acc: 0.329000; val_acc: 0.241000\n",
      "(Iteration 81 / 200) loss: 1.937518\n",
      "(Iteration 91 / 200) loss: 1.820391\n",
      "(Iteration 101 / 200) loss: 1.956579\n",
      "(Iteration 111 / 200) loss: 1.744161\n",
      "(Epoch 3 / 5) train acc: 0.393000; val_acc: 0.283000\n",
      "(Iteration 121 / 200) loss: 1.703213\n",
      "(Iteration 131 / 200) loss: 1.614523\n",
      "(Iteration 141 / 200) loss: 1.691500\n",
      "(Iteration 151 / 200) loss: 1.541082\n",
      "(Epoch 4 / 5) train acc: 0.409000; val_acc: 0.301000\n",
      "(Iteration 161 / 200) loss: 1.767786\n",
      "(Iteration 171 / 200) loss: 1.771181\n",
      "(Iteration 181 / 200) loss: 1.476462\n",
      "(Iteration 191 / 200) loss: 1.697926\n",
      "(Epoch 5 / 5) train acc: 0.428000; val_acc: 0.319000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 2.782110\n",
      "(Epoch 0 / 5) train acc: 0.125000; val_acc: 0.113000\n",
      "(Iteration 11 / 200) loss: 2.200779\n",
      "(Iteration 21 / 200) loss: 1.946160\n",
      "(Iteration 31 / 200) loss: 1.892836\n",
      "(Epoch 1 / 5) train acc: 0.305000; val_acc: 0.273000\n",
      "(Iteration 41 / 200) loss: 1.922149\n",
      "(Iteration 51 / 200) loss: 1.878036\n",
      "(Iteration 61 / 200) loss: 1.812714\n",
      "(Iteration 71 / 200) loss: 2.131547\n",
      "(Epoch 2 / 5) train acc: 0.394000; val_acc: 0.302000\n",
      "(Iteration 81 / 200) loss: 1.796904\n",
      "(Iteration 91 / 200) loss: 1.735190\n",
      "(Iteration 101 / 200) loss: 1.752295\n",
      "(Iteration 111 / 200) loss: 1.624638\n",
      "(Epoch 3 / 5) train acc: 0.426000; val_acc: 0.330000\n",
      "(Iteration 121 / 200) loss: 1.720164\n",
      "(Iteration 131 / 200) loss: 1.540599\n",
      "(Iteration 141 / 200) loss: 1.591565\n",
      "(Iteration 151 / 200) loss: 1.447923\n",
      "(Epoch 4 / 5) train acc: 0.459000; val_acc: 0.330000\n",
      "(Iteration 161 / 200) loss: 1.376142\n",
      "(Iteration 171 / 200) loss: 1.415992\n",
      "(Iteration 181 / 200) loss: 1.387422\n",
      "(Iteration 191 / 200) loss: 1.357321\n",
      "(Epoch 5 / 5) train acc: 0.528000; val_acc: 0.356000\n",
      "\n",
      "sgd\n",
      "sgd_momentum\n"
     ]
    },
    {
     "data": {
      "image/png": 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E8hzQich0Efl3EXlRRF4Qka9YbHe2iOzLbvMf3odKRERERERE+ap8PHcQwDVKqT0iMhFA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\nDmocxf4uxOH4BfGe4/A+yD0R6VBKNbr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59gNXYG7HdUgbesMdWrgWB1ettHkm5WNAR0RE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\nBjpJkiRJqqcMdJIkSZJUT/1/Z5EaW0thfZwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff58e5e850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "    print 'running with ', update_rule\n",
    "    model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "    solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-2,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "    solvers[update_rule] = solver\n",
    "    solver.train()\n",
    "    print\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.iteritems():\n",
    "    print update_rule\n",
    "    plt.subplot(3, 1, 1)\n",
    "    plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "\n",
    "    plt.subplot(3, 1, 2)\n",
    "    plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "    plt.subplot(3, 1, 3)\n",
    "    plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "\n",
    "for i in [1, 2, 3]:\n",
    "    plt.subplot(3, 1, i)\n",
    "    plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.52468751104e-08\n",
      "cache error:  2.64779558072e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation; you should see errors less than 1e-7\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "print 'next_w error: ', rel_error(expected_next_w, next_w)\n",
    "print 'cache error: ', rel_error(expected_cache, config['cache'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  1.13956917985e-07\n",
      "v error:  4.20831403811e-09\n",
      "m error:  4.21496319311e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation; you should see errors around 1e-7 or less\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "print 'next_w error: ', rel_error(expected_next_w, next_w)\n",
    "print 'v error: ', rel_error(expected_v, config['v'])\n",
    "print 'm error: ', rel_error(expected_m, config['m'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: 3.108281\n",
      "(Epoch 0 / 5) train acc: 0.104000; val_acc: 0.110000\n",
      "(Iteration 11 / 200) loss: 2.258355\n",
      "(Iteration 21 / 200) loss: 2.107197\n",
      "(Iteration 31 / 200) loss: 1.935928\n",
      "(Epoch 1 / 5) train acc: 0.337000; val_acc: 0.298000\n",
      "(Iteration 41 / 200) loss: 1.703778\n",
      "(Iteration 51 / 200) loss: 1.731835\n",
      "(Iteration 61 / 200) loss: 1.747523\n",
      "(Iteration 71 / 200) loss: 1.682030\n",
      "(Epoch 2 / 5) train acc: 0.424000; val_acc: 0.351000\n",
      "(Iteration 81 / 200) loss: 1.548294\n",
      "(Iteration 91 / 200) loss: 1.559627\n",
      "(Iteration 101 / 200) loss: 1.441254\n",
      "(Iteration 111 / 200) loss: 1.465607\n",
      "(Epoch 3 / 5) train acc: 0.489000; val_acc: 0.361000\n",
      "(Iteration 121 / 200) loss: 1.695122\n",
      "(Iteration 131 / 200) loss: 1.280723\n",
      "(Iteration 141 / 200) loss: 1.415465\n",
      "(Iteration 151 / 200) loss: 1.315037\n",
      "(Epoch 4 / 5) train acc: 0.524000; val_acc: 0.359000\n",
      "(Iteration 161 / 200) loss: 1.311867\n",
      "(Iteration 171 / 200) loss: 1.477055\n",
      "(Iteration 181 / 200) loss: 1.248163\n",
      "(Iteration 191 / 200) loss: 1.243190\n",
      "(Epoch 5 / 5) train acc: 0.591000; val_acc: 0.390000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.368688\n",
      "(Epoch 0 / 5) train acc: 0.157000; val_acc: 0.155000\n",
      "(Iteration 11 / 200) loss: 2.022018\n",
      "(Iteration 21 / 200) loss: 1.915857\n",
      "(Iteration 31 / 200) loss: 1.877239\n",
      "(Epoch 1 / 5) train acc: 0.389000; val_acc: 0.298000\n",
      "(Iteration 41 / 200) loss: 1.695895\n",
      "(Iteration 51 / 200) loss: 1.784834\n",
      "(Iteration 61 / 200) loss: 1.646640\n",
      "(Iteration 71 / 200) loss: 1.527139\n",
      "(Epoch 2 / 5) train acc: 0.426000; val_acc: 0.314000\n",
      "(Iteration 81 / 200) loss: 1.526037\n",
      "(Iteration 91 / 200) loss: 1.669815\n",
      "(Iteration 101 / 200) loss: 1.738287\n",
      "(Iteration 111 / 200) loss: 1.602427\n",
      "(Epoch 3 / 5) train acc: 0.447000; val_acc: 0.331000\n",
      "(Iteration 121 / 200) loss: 1.481980\n",
      "(Iteration 131 / 200) loss: 1.357772\n",
      "(Iteration 141 / 200) loss: 1.475960\n",
      "(Iteration 151 / 200) loss: 1.383540\n",
      "(Epoch 4 / 5) train acc: 0.519000; val_acc: 0.341000\n",
      "(Iteration 161 / 200) loss: 1.337665\n",
      "(Iteration 171 / 200) loss: 1.592213\n",
      "(Iteration 181 / 200) loss: 1.435196\n",
      "(Iteration 191 / 200) loss: 1.456955\n",
      "(Epoch 5 / 5) train acc: 0.525000; val_acc: 0.353000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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TUlLCs88+yymnnNJZl5aSvFmzKPrBAlzFxWAMruJiin6wgLxZs9p9zp4ObL/4xS/y5JNP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TAAAg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\n4r+pJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f68719c6ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "    print 'running with ', update_rule\n",
    "    model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "    solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "    solvers[update_rule] = solver\n",
    "    solver.train()\n",
    "    print\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.iteritems():\n",
    "    plt.subplot(3, 1, 1)\n",
    "    plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "\n",
    "    plt.subplot(3, 1, 2)\n",
    "    plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "    plt.subplot(3, 1, 3)\n",
    "    plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "\n",
    "for i in [1, 2, 3]:\n",
    "    plt.subplot(3, 1, i)\n",
    "    plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a good model!\n",
    "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
    "\n",
    "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# batch normalization and dropout useful. Store your best model in the         #\n",
    "# best_model variable.                                                         #\n",
    "################################################################################\n",
    "best_model = FullyConnectedNet([100, 100, 100, 100, 100, 100], weight_scale=1e-5, \n",
    "                              use_batchnorm=True, dropout=0.8)\n",
    "\n",
    "solver = Solver(best_model, data,\n",
    "                num_epochs=20, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={'learning_rate': 1e-4},\n",
    "                verbose=False)\n",
    "solver.train()\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test you model\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.537\n",
      "Test set accuracy:  0.532\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print 'Validation set accuracy: ', (y_val_pred == data['y_val']).mean()\n",
    "print 'Test set accuracy: ', (y_test_pred == data['y_test']).mean()"
   ]
  }
 ],
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